no code implementations • 11 Mar 2023 • Lucas Lamata
This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning.
no code implementations • 4 Jan 2022 • Eduardo Reck Miranda, Satvik Venkatesh, Jose D. Martın-Guerrero, Carlos Hernani-Morales, Lucas Lamata, Enrique Solano
At the time of writing, available quantum computing hardware and brain activity sensing technology are not sufficiently developed for real-time control of quantum states with the brain.
no code implementations • 22 Feb 2021 • Jie Peng, Juncong Zheng, Jing Yu, Pinghua Tang, G. Alvarado Barrios, Jianxin Zhong, Enrique Solano, F. Albarran-Arriagada, Lucas Lamata
General solutions to the quantum Rabi model involve subspaces with unbounded number of photons.
Quantum Physics Optics
no code implementations • 25 Apr 2020 • Lucas Lamata
Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies.
1 code implementation • 11 Apr 2019 • Ana Martin, Bruno Candelas, Ángel Rodríguez-Rozas, José D. Martín-Guerrero, Xi Chen, Lucas Lamata, Román Orús, Enrique Solano, Mikel Sanz
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates.
Quantum Physics Mesoscale and Nanoscale Physics
no code implementations • 27 Jul 2018 • Yongcheng Ding, Lucas Lamata, Mikel Sanz, Xi Chen, Enrique Solano
Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information.
2 code implementations • 12 Jul 2018 • Tao Xin, Shijie Wei, Jianlian Cui, Junxiang Xiao, Iñigo Arrazola, Lucas Lamata, Xiangyu Kong, Dawei Lu, Enrique Solano, Guilu Long
We present and experimentally realize a quantum algorithm for efficiently solving the following problem: given an $N\times N$ matrix $\mathcal{M}$, an $N$-dimensional vector $\textbf{\emph{b}}$, and an initial vector $\textbf{\emph{x}}(0)$, obtain a target vector $\textbf{\emph{x}}(t)$ as a function of time $t$ according to the constraint $d\textbf{\emph{x}}(t)/dt=\mathcal{M}\textbf{\emph{x}}(t)+\textbf{\emph{b}}$.
Quantum Physics
no code implementations • 18 Jan 2017 • Lucas Lamata
Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops.
no code implementations • 16 Dec 2016 • Unai Alvarez-Rodriguez, Lucas Lamata, Pablo Escandell-Montero, José D. Martín-Guerrero, Enrique Solano
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations.